Untitled

import numpy

# Local Neighbors Measure
def LNM(word: str, n_neighbors=10):

  word_1960 = e_1960.wv[word]
  word_2010 = e_2010.wv[word]
  neighbors_1960, neighbors_2010 = [], []

  # adding neighbors of 1960
  neighbors_1960.extend([w[0] for w in e_1960.wv.most_similar(positive=[word_1960], topn=30)])
  # adding neighbors of 2010
  neighbors_2010.extend([w[0] for w in e_2010.wv.most_similar(positive=[word_2010], topn=30)])

  neighbors = []
  
  cnt = 0
  for nb in neighbors_1960:
    try:
      # controllo se la parola esiste nel 2010
      _ = e_2010.wv[nb]
      neighbors.append(e_1960.wv[nb])
      cnt += 1
      if cnt == n_neighbors - 1:
        break
    except:
      continue

  cnt = 0
  for nb in neighbors_2010:
    try:
      # controllo se la parola esiste nel 1960
      _ = e_1960.wv[nb]
      neighbors.append(e_2010.wv[nb])
      cnt += 1
      if cnt == n_neighbors - 1:
        break
    except:
      continue

  # word similarity with each neighbor
  sim_1960 = []
  sim_2010 = []
  for nb in neighbors:
    sim_1960.append(1-cosine(nb, word_1960))
    sim_2010.append(1-cosine(nb, word_2010))

  # calculate and return the similarity between the two vectors
  return 1 - cosine(np.array(sim_1960), np.array(sim_2010))